scholarly journals Combining Spectral Water Indices and Mathematical Morphology to Evaluate Surface Water Extraction in Taiwan

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2774
Author(s):  
Fang-Shii Ning ◽  
Yu-Chan Lee
Keyword(s):  
Surface Water ◽  
Edge Detection ◽  
Mathematical Morphology ◽  
Global Climate ◽  
Threshold Value ◽  
Water Extraction ◽  
Water Index ◽  
Braided Rivers ◽  
Channel Patterns ◽  
Water Indices

Rivers in Taiwan are characterised by steep slopes and high sediment concentrations. Moreover, with global climate change, the dynamics of channel meandering have become complicated and frequent. The primary task of river governance and disaster prevention is to analyse river changes. Spectral water indices are mostly used for surface water estimation, which separates the water from the background based on a threshold value, but it can be challenging in the case of environmental noise. Edge detection uses a canny edge detector and mathematical morphology for extracting geometrical features from the image and effective edge detection. This study combined spectral water indices and mathematical morphology to capture water bodies based on downloaded remote sensing images. From the findings, this study summarised the applicability of various spectral water body indices to the surface water extraction of different river channel patterns in Taiwan. The normalised difference water index and the modified normalised difference water index are suitable for braided rivers, whereas the automated water extraction index is ideal for meandering rivers.

scholarly journals Evaluation of Water Indices for Surface Water Extraction in a Landsat 8 Scene of Nepal

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2580 ◽  
Author(s):  
Tri Acharya ◽  
Anoj Subedi ◽  
Dong Lee
Keyword(s):  
Surface Water ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Pure Water ◽  
Confusion Matrix ◽  
Threshold Value ◽  
Environmental Noise ◽  
Water Extraction ◽  
Landsat 8 ◽  
Water Indices

Accurate and frequent updates of surface water have been made possible by remote sensing technology. Index methods are mostly used for surface water estimation which separates the water from the background based on a threshold value. Generally, the threshold is a fixed value, but can be challenging in the case of environmental noise, such as shadow, forest, built-up areas, snow, and clouds. One such challenging scene can be found in Nepal where no such evaluation has been done. Taking that in consideration, this study evaluates the performance of the most widely used water indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Automated Water Extraction Index (AWEI) in a Landsat 8 scene of Nepal. The scene, ranging from 60 m to 8848 m, contains various types of water bodies found in Nepal with different forms of environmental noise. The evaluation was conducted based on measures from a confusion matrix derived using validation points. Comparing visually and quantitatively, not a single method was able to extract surface water in the entire scene with better accuracy. Upon selecting optimum thresholds, the overall accuracy (OA) and kappa coefficient (kappa) was improved, but not satisfactory. NDVI and NDWI showed better results for only pure water pixels, whereas MNDWI and AWEI were unable to reject snow cover and shadows. Combining NDVI with NDWI and AWEI with shadow improved the accuracy but inherited the NDWI and AWEI characteristics. Segmenting the test scene with elevations above and below 665 m, and using NDVI and NDWI for detecting water, resulted in an OA of 0.9638 and kappa of 0.8979. The accuracy can be further improved with a smaller interval of categorical characteristics in one or multiple scenes.

scholarly journals Accurate extraction of surface water in complex environment based on Google Earth Engine and Sentinel-2

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253209
Author(s):  
Jianfeng Li ◽  
Biao Peng ◽  
Yulu Wei ◽  
Huping Ye
Keyword(s):  
Surface Water ◽  
Google Earth ◽  
Water Extraction ◽  
Practical Significance ◽  
Commission Error ◽  
Complex Environment ◽  
Visual Interpretation ◽  
Water Index ◽  
Google Earth Engine ◽  
Sentinel 2

To realize the accurate extraction of surface water in complex environment, this study takes Sri Lanka as the study area owing to the complex geography and various types of water bodies. Based on Google Earth engine and Sentinel-2 images, an automatic water extraction model in complex environment(AWECE) was developed. The accuracy of water extraction by AWECE, NDWI, MNDWI and the revised version of multi-spectral water index (MuWI-R) models was evaluated from visual interpretation and quantitative analysis. The results show that the AWECE model could significantly improve the accuracy of water extraction in complex environment, with an overall accuracy of 97.16%, and an extremely low omission error (0.74%) and commission error (2.35%). The AEWCE model could effectively avoid the influence of cloud shadow, mountain shadow and paddy soil on water extraction accuracy. The model can be widely applied in cloudy, mountainous and other areas with complex environments, which has important practical significance for water resources investigation, monitoring and protection.

scholarly journals Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors

Water ◽  
2017 ◽  
Vol 9 (4) ◽  
pp. 256 ◽  
Author(s):  
Yan Zhou ◽  
Jinwei Dong ◽  
Xiangming Xiao ◽  
Tong Xiao ◽  
Zhiqi Yang ◽  
...  
Keyword(s):  
Surface Water ◽  
Water Body ◽  
Landsat 8 ◽  
Open Surface ◽  
Body Mapping ◽  
Water Index ◽  
Landsat 7 ◽  
Surface Water Bodies ◽  
Water Indices ◽  
Sentinel 2

Open surface water bodies play an important role in agricultural and industrial production, and are susceptible to climate change and human activities. Remote sensing data has been increasingly used to map open surface water bodies at local, regional, and global scales. In addition to image statistics-based supervised and unsupervised classifiers, spectral index- and threshold-based approaches have also been widely used. Many water indices have been proposed to identify surface water bodies; however, the differences in performances of these water indices as well as different sensors on water body mapping are not well documented. In this study, we reviewed and compared existing open surface water body mapping approaches based on six widely-used water indices, including the tasseled cap wetness index (TCW), normalized difference water index (NDWI), modified normalized difference water index (mNDWI), sum of near infrared and two shortwave infrared bands (Sum457), automated water extraction index (AWEI), land surface water index (LSWI), as well as three medium resolution sensors (Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI). A case region in the Poyang Lake Basin, China, was selected to examine the accuracies of the open surface water body maps from the 27 combinations of different algorithms and sensors. The results showed that generally all the algorithms had reasonably high accuracies with Kappa Coefficients ranging from 0.77 to 0.92. The NDWI-based algorithms performed slightly better than the algorithms based on other water indices in the study area, which could be related to the pure water body dominance in the region, while the sensitivities of water indices could differ for various water body conditions. The resultant maps from Landsat 8 and Sentinel-2 data had higher overall accuracies than those from Landsat 7. Specifically, all three sensors had similar producer accuracies while Landsat 7 based results had a lower user accuracy. This study demonstrates the improved performance in Landsat 8 and Sentinel-2 for open surface water body mapping efforts.

scholarly journals Evaluation of Machine Learning Algorithms for Surface Water Extraction in a Landsat 8 Scene of Nepal

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2769 ◽  
Author(s):  
Tri Dev Acharya ◽  
Anoj Subedi ◽  
Dong Ha Lee
Keyword(s):  
Machine Learning ◽  
Surface Water ◽  
Recursive Partitioning ◽  
Learning Algorithms ◽  
High Elevation ◽  
Machine Learning Algorithms ◽  
Water Extraction ◽  
Support Vector ◽  
Landsat 8 ◽  
Water Index

With over 6000 rivers and 5358 lakes, surface water is one of the most important resources in Nepal. However, the quantity and quality of Nepal’s rivers and lakes are decreasing due to human activities and climate change. Despite the advancement of remote sensing technology and the availability of open access data and tools, the monitoring and surface water extraction works has not been carried out in Nepal. Single or multiple water index methods have been applied in the extraction of surface water with satisfactory results. Extending our previous study, the authors evaluated six different machine learning algorithms: Naive Bayes (NB), recursive partitioning and regression trees (RPART), neural networks (NNET), support vector machines (SVM), random forest (RF), and gradient boosted machines (GBM) to extract surface water in Nepal. With three secondary bands, slope, NDVI and NDWI, the algorithms were evaluated for performance with the addition of extra information. As a result, all the applied machine learning algorithms, except NB and RPART, showed good performance. RF showed overall accuracy (OA) and kappa coefficient (Kappa) of 1 for the all the multiband data with the reference dataset, followed by GBM, NNET, and SVM in metrics. The performances were better in the hilly regions and flat lands, but not well in the Himalayas with ice, snow and shadows, and the addition of slope and NDWI showed improvement in the results. Adding single secondary bands is better than adding multiple in most algorithms except NNET. From current and previous studies, it is recommended to separate any study area with and without snow or low and high elevation, then apply machine learning algorithms in original Landsat data or with the addition of slopes or NDWI for better performance.

scholarly journals Wireless Network Sensing of Urban Surface Water Environment Based on Clustering Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Qichao Zhao ◽  
Xiufeng Yang ◽  
Xuxin Dong ◽  
Huairui Li
Keyword(s):  
Surface Water ◽  
Fuzzy Clustering ◽  
Classification Accuracy ◽  
Water Environment ◽  
Kappa Coefficient ◽  
Experimental Results ◽  
Water Extraction ◽  
Urban Surface ◽  
Wireless Sensing ◽  
Water Index

To improve the wireless sensing image extraction technology of urban surface water environment, a regional FCM clustering method combined with water index was proposed in this paper. The normalized water index (NDWI) was obtained by calculating the fusion multispectral wireless sensing image. Through the combination with normalized water index, fuzzy clustering results were obtained by RFCM algorithm proposed in this paper. The optimal threshold was selected to defuzzify the fuzzy clustering results, and finally, the extraction results of urban surface water were obtained. The accuracy of the proposed algorithm was compared with that of the traditional surface water extraction algorithm. The experimental results showed that the size of different neighborhood regions affected the water extraction accuracy. In W city, the kappa coefficient of MFCM16 was 0.41% higher than that of MFCM8, and the overall classification accuracy of MFCM16 was 1.33% higher than that of MFCM. In G city area, the kappa coefficient of MFCM16 was 1.81% higher than that of MFCM8, and the overall classification accuracy of MFCM16 was 1.7% higher than that of MFCM. Comparing the RFCM algorithm with other algorithms, the RFCM algorithm obtained the best experimental results, to reduce the “salt-and-pepper phenomenon” effect.

scholarly journals ASSESSMENT OF SURFACE WATER DYNAMICS USING MULTIPLE WATER INDICES AROUND ADAMA WOREDA, ETHIOPIA

2018 ◽  
Vol IV-5 ◽  
pp. 181-188
Author(s):  
M. Sathianarayanan
Keyword(s):  
Surface Water ◽  
Surface Area ◽  
Rapid Change ◽  
Water Bodies ◽  
Water Dynamics ◽  
Landsat 8 ◽  
Water Index ◽  
Surface Water Resources ◽  
Normalized Difference Water Index ◽  
Water Indices

<p><strong>Abstract.</strong> Rapid change of Adama wereda during the last three decades has posed a serious threat to the existence of ecological systems, specifically water bodies which play a crucial part in supporting life. Role of Satellite images in Remote Sensing could be more important in investigation, monitoring dynamically and planning of natural surface water resources. Landsat-5(TM) &amp;amp; Landsat 8 (OLI) has high spatial, temporal and multispectral resolution and therefore provides consistent and perfect data to detect changes in surface changes of water bodies. In this paper, a study was conducted to detect the changes in water body extent during the period of 1984, 2000 and 2017 using various water indices such as namely Water Ratio Index (WRI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), supervised classification and wetness component of K-T transformation and the results are Presented. NDWI has been adopted for this study as compared with other indices through ground survey. The results showed an intense decreasing trend in the lakes of chelekleka, kiroftu, lake 1 and lake 3 of surface area in the period 1984–2017, especially between 2000 and 2017 when the lake lost about 1.309<span class="thinspace"></span>km<sup>2</sup> (one third) of its surface area compared to the year 2000, which is equivalent to 76%, 18%, 0.03% and 96%. Interestingly koka lake has shown very erratic changes in its area coverage by losing almost 3.5<span class="thinspace"></span>km<sup>2</sup> between 1984 and 2000 and then climbing back up by 14.8<span class="thinspace"></span>km<sup>2</sup> in 2017. Percentage of increment was observed that 10.6% as compared with previous year.</p>

scholarly journals PHÂN TÍCH CÁC CHỈ SỐ NƯỚC TỪ ẢNH VỆ TINH LANDSAT 8 OLI ĐỂ CHIẾT TÁCH ĐƯỜNG BỜ BIỂN KHU VỰC TÂY NAM VIỆT NAM

2019 ◽  
Vol 18 (4) ◽  
pp. 339-349
Author(s):  
Tran Anh Tuan ◽  
Le Dinh Nam ◽  
Nguyen Thi Anh Nguyet ◽  
Pham Viet Hong ◽  
Nguyen Thi Ai Ngan ◽  
...  
Keyword(s):  
Remote Sensing Data ◽  
Threshold Value ◽  
Absolute Error ◽  
Acquisition Time ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Coast Of Vietnam ◽  
Water Indices

The paper presents results of analysis of water indices using remote sensing data to extract an instantaneous shoreline at the time of image acquisition on the southwest coast of Vietnam. The water indices as NDWI (Normalized Difference Water Index), MNDWI (Modified Normalized Difference Water Index), and AWEI (Automated Water Extraction Index) were calculated from Landsat 8 OLI imagery. Then, an extracted distribution histogram of water indices’ values in the study area was used to separate the land from the sea. The position having abnormal frequency of pixels on the histogram is the threshold value to determine the boundary of land and water, and it is considered the shoreline. The study showed the threshold values of NDWI, MNDWI and AWEI which were defined at 0.12, 0.17 and 0.18 respectively. The precision of shoreline extraction from each respective water index was verified by field survey data using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) methods. The verified results showed that MAE and MSE of the shorelines extracted from all three water indices were lower than an allowed limit of 30 m (equivalent to spatial resolution of the Landsat 8 image). However, the shoreline extracted from AWEI had the highest accuracy and it was considered the most appropriate shoreline at the acquisition time of image.

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